Book Image

Neural Network Programming with Java - Second Edition

By : Fabio M. Soares, Alan M. F. Souza
Book Image

Neural Network Programming with Java - Second Edition

By: Fabio M. Soares, Alan M. F. Souza

Overview of this book

<p>Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.</p> <p>You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.</p> <p>All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.</p>
Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


In this chapter, we've an application of customer profiling using the Kohonen neural network. Unlike the classification task, the clustering task does not consider the previous knowledge on the desired output; instead it is desirable for the clusters to be found by the neural network. However, we've seen that validation techniques may include external validation, which is a comparison with what could be understood as target output. Customer profiling is important because it gives a business owner more accurate and clean information about their customers, without the human interference in pointing which customers are in some groups or in others, as occurs in supervised learning. That's the advantage of unsupervised learning, enabling the data to draw results solely by themselves.